Efficient molecular subtype classification of high‐grade serous ovarian cancer |
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Authors: | Huei San Leong Laura Galletta Dariush Etemadmoghadam Joshy George The Australian Ovarian Cancer Study Martin Köbel Susan J Ramus David Bowtell |
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Institution: | 1. The Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia;2. Department of Pathology, University of Melbourne, Melbourne, Victoria, Australia;3. Sir Peter MacCallum Cancer Centre Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia;4. The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA;5. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia;6. Centre for Cancer Research, University of Sydney at Westmead Millennium Institute, and Department ofGynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia;7. Department of Laboratory Medicine and Pathology, University of Calgary, Calgary, Alberta, Canada;8. Department of Preventive Medicine, Keck School of Medicine, USC/Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA;9. The Department of Biochemistry, University of Melbourne, Parkville, Victoria, Australia;10. Hammersmith Hospital, Imperial College, London, UK |
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Abstract: | High‐grade serous carcinomas (HGSCs) account for approximately 70% of all epithelial ovarian cancers diagnosed. Using microarray gene expression profiling, we previously identified four molecular subtypes of HGSC: C1 (mesenchymal), C2 (immunoreactive), C4 (differentiated), and C5 (proliferative), which correlate with patient survival and have distinct biological features. Here, we describe molecular classification of HGSC based on a limited number of genes to allow cost‐effective and high‐throughput subtype analysis. We determined a minimal signature for accurate classification, including 39 differentially expressed and nine control genes from microarray experiments. Taqman‐based (low‐density arrays and Fluidigm), fluorescent oligonucleotides (Nanostring), and targeted RNA sequencing (Illumina) assays were then compared for their ability to correctly classify fresh and formalin‐fixed, paraffin‐embedded samples. All platforms achieved > 90% classification accuracy with RNA from fresh frozen samples. The Illumina and Nanostring assays were superior with fixed material. We found that the C1, C2, and C4 molecular subtypes were largely consistent across multiple surgical deposits from individual chemo‐naive patients. In contrast, we observed substantial subtype heterogeneity in patients whose primary ovarian sample was classified as C5. The development of an efficient molecular classifier of HGSC should enable further biological characterization of molecular subtypes and the development of targeted clinical trials. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. |
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Keywords: | serous ovarian cancer molecular subtypes classification TaqMan low‐density arrays Nanostring Fluidigm Illumina targeted RNA expression |
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